Omid-Nejati/Locality-iN-Locality
Robust Transformer with Locality Inductive Bias and Feature Normalization (JESTECH 2023)
This project provides a robust way to analyze complex data patterns, specifically using 'Transformers' which are powerful AI models. It helps researchers and AI practitioners develop more reliable machine learning systems by processing raw data and outputting improved model performance or more accurate predictions, especially in areas like image recognition or natural language processing.
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Use this if you are a machine learning researcher or AI engineer working with transformer models and need to improve their robustness and performance.
Not ideal if you are looking for a ready-to-use application or a low-code solution for general data analysis, as this requires expertise in machine learning model development.
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Python
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Last pushed
Jul 14, 2024
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